1. Field of Invention
This invention relates to recognizing objects appearing within an image.
2. Description of Related Art
Object recognition is important in systems performing object inspection, security and/or authentication functions, among other uses. Object recognition is complicated, in that objects typically have substantially different shapes depending on the angle they are viewed from. For example, as a circle is viewed from increasingly oblique angles, its shape becomes increasingly elliptical. Similarly, a square or rectangle, when viewed from increasingly oblique angles, becomes trapezoidal or rhombic, depending on whether an edge or vertex is closest to the observer. For more complicated objects, such as faces, as the viewing angle becomes more oblique, it is difficult for even a human to discern the basic shape of such objects. For computer-based or automated image inspection and/or analysis systems and methods, extracting and identifying a shape, especially a complicated shape such as a face, viewed at an unknown angle is often impossible.
In particular, computer based and/or automated image analysis systems and methods typically identify objects in an image by matching the extracted or segmented image to an object template, where each different template corresponds to a different physical object. One way to do this matching is by using invariant-based templates and matching techniques. An invariant of an image of an object is a parameter derived from some aspect of the object image whose value does not change as the image of the object changes, i.e., does not vary as the object image varies.
There are several types of image invariants. “Projective invariants of shapes”, I. Weiss, Proceedings CVPR '88, 1988, discloses using invariants in computer vision systems. As disclosed in “Invariance-a new framework for vision”, D. Forsyth et al., Proceedings, Third International Conference Computer Vision, 1990, algebraic invariants, which are obtained by fitting polynomials to an image of an object and determining the algebraic invariant using the polynomial coefficients, have been applied to recognize industrial objects in an image. However, algebraic invariants suffer from several shortcomings. First, most objects cannot be expressed in terms of simple polynomials. Second, algebraic invariants are a global method. That is, they require, for whatever shape has been used to define the value of the algebraic invariant, that entire shape be available when determining the value of the algebraic invariant from the image data. Thus, they will not work when even a small portion of the defined shape of the object is hidden from view in the image.
Differential invariants, which are also referred to as local invariants in this field and which are obtained by using derivatives to produce invariant features for points on a curve, also suffer from fundamental shortcomings. That is, differential invariants depend on high-order derivatives. Thus, differential invariants are particularly sensitive to noise and round-off error. Various techniques based on semi-differential invariants, “noise-resistant” differential invariants, and others have been introduced to reduce this noise sensitivity. Similarly, various techniques based on integral invariants have been developed to overcome the limitations of differential invariants.